Medical system for therapy adjustment

ABSTRACT

Methods and systems for seamless adjustment of treatment are disclosed. A determination can be made as to whether to intervene with a patient&#39;s treatment based on data obtained from implantable electrodes and/or non-implantable electrodes. The data from non-implantable electrodes have a correction factor applied to adjust for less accuracy compared to data acquired from implantable electrodes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a Divisional application of U.S. patentapplication Ser. No. 16/394,942, filed Apr. 25, 2019, which claimspriority to, and the benefit of, U.S. Provisional Patent ApplicationSer. No. 62/663,055, filed on Apr. 26, 2018, the entire contents ofwhich are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to a medical system, and, moreparticularly, to a medical system configured to determine whether tointervene with a patient's treatment.

BACKGROUND

Chronic heart failure (CHF) is a serious condition that occurs when aheart is unable to consistently pump blood at an adequate rate. Toimprove the ability of the heart to efficiently pump blood, CHF patientsmay require an implantable medical device (IMD). IMDs such asimplantable cardioverter defibrillators (ICDs) or pacemakers are capableof delivering cardiac resynchronization therapy for improving a CHFpatient's heart function. Despite using IMDs to improve heart function,CHF patients may progressively deteriorate, as evidenced by weight gain,change in blood pressure, malaise, fatigue, swelling in legs and feet,fainting, and/or palpitations.

Patient data are obtained in a variety of ways. Typically, a patientdirectly conveys health data to medical personnel during an officevisit. Some data may be automatically generated and sent over theInternet to a computer system or health care system. For example,electronic weight scales are configured to weigh a patient and thenautomatically transmit that data to the health care system.

In response to the collected data, healthcare systems can respond in avariety of ways. Some healthcare systems are able to generate healthalerts based upon data detected by an IMD. One exemplary healthcaresystem relates to US Patent Application US 2010-0030293 A1 to Sarkar etal. that is capable of generating alerts for a patient to seek medicaltreatment in response to detected information. For example, a medicaldevice may detect worsening heart failure in the patient based on adiagnostic parameter. Upon detecting worsening heart failure, themedical device may, for example, provide an alert that enables thepatient to seek medical attention before experiencing a heart failureevent.

While numerous healthcare systems are able to automatically notifyhealth care workers of potential health issues such as that which isdescribed in US Patent Application US 2010-0030293 A1 to Sarkar et al.,a healthcare system typically requires a physician's input to adjusttherapy (i.e. medication) delivered to a patient. It is desirable todevelop a healthcare system that is able to seamlessly respond to apatient's deteriorating health conditions without directly contacting aphysician.

SUMMARY OF THE DISCLOSURE

Methods and systems are disclosed for managing therapy delivered to apatient. One or more embodiments comprise a system that includes animplantable medical device comprising one or more electrodes configuredto be implanted within a patient's body, to acquire first signalscorresponding to signals sensed from within the patient's body and togenerate first data transmissions in response to the acquired firstsignals. The system further includes a wearable device comprising one ormore electrodes is configured to be positioned in contact with anexternal surface of the patient's body, to acquire second signalscorresponding to signals sensed from the external surface of thepatient's body, and to generate second data transmissions in response tothe acquired second signals. The system further includes an input/outputdevice that is configured to receive the first data transmissions andthe second data transmissions. The system also includes one or moreprocessors that are configured to: (1) receive the first datatransmissions and the second data transmissions, (2) compare thereceived first data transmissions and the received second datatransmissions to one or more thresholds, (3) determine whether data ofthe received first data transmissions and the received second datatransmissions is indicative of a heart failure (HF) worsening episodebased on the comparing, and (4) adjust a patient's therapy in responseto the HF worsening episode being indicated.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example computer system thatincludes an external device and one or more computing devices that arecoupled to the IMD and programmer shown in FIG. 1 via a network.

FIG. 2 is a diagram of the exemplary IMD shown in FIG. 1.

FIG. 3 is a functional block diagram of the exemplary IMD shown in FIG.1.

FIG. 4 is a flow diagram of an exemplary symptom management interventionprocess controlled by a medical system that can cause one or moreadjustments to therapy that is being delivered to a patient.

FIG. 5 is a block diagram of integrated diagnostics related to riskstatus.

FIG. 6 is a diagram depicting exemplary recovery on a daily basisrelative to a reference threshold.

FIG. 7 depicts medium risk status relative to a set of exemplary riskfactors.

FIG. 8 depicts high risk status relative to a set of exemplary riskfactors.

FIG. 9 is a block diagram of a patient's medication dispenser.

FIG. 10 is a flow diagram of an exemplary method of using data acquiredfrom an implantable medical device and a wearable device.

FIG. 11A depicts an HTN pacing protocol.

FIG. 11B shows reduction in blood pressure (second bar from left) duringan HTN pacing protocol compared to control.

FIG. 12 is a flow diagram of an exemplary closed loop method of using apacemaker to control blood pressure using pacing for HTN therapy andpulse transit time measurement

DETAILED DESCRIPTION

Exemplary systems, methods, and interfaces shall be described withreference to FIGS. 1-12. It will be apparent to one skilled in the artthat elements or processes from one embodiment may be used incombination with elements or processes of the other embodiments, andthat the possible embodiments of such methods, apparatus, and systemsusing combinations of features set forth herein is not limited to thespecific embodiments shown in the figures and/or described herein.Further, it will be recognized that the embodiments described herein mayinclude many elements that are not necessarily shown to scale. Stillfurther, it will be recognized that timing of the processes and the sizeand shape of various elements herein may be modified but still fallwithin the scope of the present disclosure, although certain timings,one or more shapes and/or sizes, or types of elements, may beadvantageous over others.

FIGS. 1-3 disclose a system for intervening into the therapy deliveredto the patient while FIG. 4 discloses a flow diagram, controlled by thesystem, for an intervention to modify the therapy delivered to apatient.

FIG. 1 is a block diagram illustrating an exemplary computer system 100that can seamlessly trigger the adjustment of a patient's treatment planwithout directly communicating with the patient's physician any timeafter the treatment plan has been sent to a centralized communicationcenter for storage or stored into a memory of a computing device. Anexemplary system is shown and described in U.S. patent application Ser.No. 15/402,839, filed on Jan. 10, 2017, the disclosure of which isincorporated herein by reference in its entirety. The treatment plan,stored at the centralized communication center or in the memory of aserver, can comprise one or more rounds of medication (e.g., a firstround of medication, a second round of medication etc.). Generally,adjusting treatment of the patient depends on the patient's risk of a HFevent, and data acquired from IMD 16, computing devices 102 a-n and/orprogrammer 24. A HF event is when a patient was admitted to the hospitalfor worsening HF or the patient has received Intravenous HF therapy(e.g. IV diuretics/vasodilators), ultrafiltration at any settingsincluding an emergency department, ambulance, observation unit, urgentcare, HF/Cardiology Clinic or the patient's home. Communication of theadjusted treatment can be delivered either electronically or via nurseto the patient.

Computer system 100 includes one or more computing devices 102 a-102 n,a programmer 24, a server 130, a network 110, and access point 112.Network 110 may generally be used to transmit information or data (e.g.,physiological data, risk level data, recovery data) between IMD 16 toother external computing devices 102 a-c. However, network 110 may alsobe used to transmit information from IMD 16 to an external computingdevice (e.g. CARELINK®). Exemplary computer systems and/or features thatcan implement the present disclosure include U.S. Pat. No. 8,585,604 toBennett et al., U.S. Pat. No. 6,970, 742 to Mann et al., Ritzema et al,Physician-Directed Patient Self-Management of Left Atrial Pressure inAdvanced Chronic Heart Failure, Circulation, 2010, U.S. Pat. No.7,577,475 to Cosentino et al, System, method, and apparatus forcombining information from an implanted device with information from apatient monitoring apparatus, 2009, the disclosure of each areincorporated by reference in their entirety.

IMD 16 may use its telemetry module 88, described below relative to FIG.3, to communicate with computing devices 102 a-n (“n” being any wholenumber of computing devices), server 130, programmer 24. Typically, awireless connection is employed. In one example of FIG. 1, access point110, programmer 24, external device 102 n, and computing devices 102a-102 n can be interconnected, and able to communicate with each other,through network 112. In some cases, one or more of access point 110,programmer 24, external device 102 n, and computing devices 102 a-102 nmay be coupled to network 112 through one or more wireless connections.

Another example of a computing device 102 n may be a patient'smedication or drug dispenser 102, as shown in FIG. 7. The computerizeddrug dispenser 102 includes a set of compartments, in which eachcompartment 103 a-d stores one or more medications at a prescribeddosage. In another embodiment, an implantable drug dispenser can be usedto deliver the proper dosage of medication. Exemplary implantable drugdispensers that can be configured to include two different compartmentsfor holding two different dosages of the same medication (e.g.diuretics, blood pressure medicine etc.). Exemplary implantable drugdispensers that can be configured to include two or more drugcompartments for release into the body are shown and described in U.S.Pat. Nos. 7,001,359, 7,054,782, 7,008,413, 7,264,611, 7,160,284, all ofwhich are incorporated by reference. In yet another embodiment, asubcutaneous drug dispenser can be used (e.g. subcutaneous implantabledevices such as a drug delivery such as the Paradign Revel fromMinimMed, or SC2 infuser by SC Pharmaceuticals) Implantable drugdispensers are further configured to receive communication signals todeliver the drug through a transceiver or transmitter. In one or moreembodiments, the implantable drug delivery device receives a commandsignal from a device (server 130 computing device e.g. cell phone) fromexternal to the device. BLUETOOTH™ technology can be used in thecommunication process to deliver wireless signals to the implantabledrug dispensing device. The implantable drug dispenser can be configuredto receive a wireless message from a computing device (e.g. Iphone™ thatthe user indicates through the graphical user interface to deliver adrug or the Iphone™ may use data acquired from external electrodesand/or implantable electrodes to deliver a drug or a dosage of drugdifferent from the previously delivered dosage of drug. In one or moreembodiments, the implantable drug dispenser is configured to receivecommand messages to adjust dosages of drug(s) delivered. In anotherembodiment, the implantable drug dispenser can be configured to signalanother implantable medical device (LINQ™, pacemaker etc.) or externaldevice (computing device, Iphone™) information (e.g. drug level isdepleting or running very low).

The drug dispenser is further configured to receive instructions fromthe server to ensure that the patient has access to the correctmedication and/or dosage of medication. Once the server determines amedication for a patient needs to be adjusted, the server automaticallysignals the computing device 102 n to automatically adjust delivery ofthe medication. For example, assume that the patient requires a reduceddosage of a medication. The server signals the computing device 102 n toadjust the dosage delivered to a patient. The computing device 102 nautomatically switches from the first to a second dosage compartmentsfor drug delivery. The medication delivery device rotates from the firstdosage compartment that stores a first dosage to the second dosagecompartment that stores a second dosage for delivery to the patient. Themedication delivery device automatically notifies the patient there hasbeen a modification in his or her dosage. The medication delivery devicethen automatically notifies the patient to take the medication duringthe day. The drug is automatically dispensed to the patient at theproper dosage. The dispenser can be set to automatically lock drugdelivery once the proper dosage has been delivered.

IMD 16, programmer 24, external device 102 n, and computing devices 102a-102 n may each comprise one or more processors, such as one or moremicroprocessors, DSPs, ASICs, FPGAs, programmable logic circuitry, orthe like, that may perform various functions and operations, such asthose described herein. Each processor can be configured to perform sometype of analog to digital conversion (ADC) so that signals can becompared to some threshold. The signal can be filtered before or afterdigitizing the signal. Other applicable signal processing may also beapplied.

Computing devices 102 a-102 n may comprise devices such as servers,computers, weight scales, portable blood pressure machines, biometricdata collecting device, a computer, a symptom assessment system, apersonal digital assistant (e.g. cell phone, iPad, or the like). In someexamples, computing devices 102 a-n may generate data that are used byserver to perform any of the various functions or operations describedherein, e.g., generate a heart failure risk status based on the patientmetric comparisons or create patient metrics from the raw metric data.HF risk status can be calculated in a number of ways, as disclosedherein. An exemplary risk score (or risk status) is described in U.S.Provisional Application 62/554,523. Computing devices 102 a-n includeinput/output device 104 c, processor 106 b and memory 108 c.

Each computing device includes an input/output device 104 a-c, aprocessor, 106 a-c, and memory 108 a-c. Input/output device 116 includesinput devices such as a keyboard, a mouse, voice input, sensor forweight, etc. and output device includes graphical user interfaces,printers and other suitable means. Processor 106 a-c or 134 includes anysuitable processor. The processor 134 can be configured to perform sometype of analog to digital conversion so that signal can be compared tosome threshold. Processor 134 is configured to perform a variety offunctions such as calculations, accessing data from memory performingcomparisons, setting the start and end dates for each evaluation periodetc. The evaluation period serves as an evaluation window thatencompasses data, acquired from each patient, that are within theboundaries (i.e. start and end times). Exemplary calculations performedby processor 106 a-c can be calculating risk of a heart failure eventfor each evaluation period.

Memory 108 a-c may include any volatile, non-volatile, magnetic,optical, or electrical media, such as a random-access memory (RAM),read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasableprogrammable ROM (EEPROM), flash memory, or any other digital or analogmedia. Memory 108 a-c stores data. Exemplary data stored in memory 108a-c includes heart failure patient data, heart failure prospective riskdata, intracardiac or intravascular pressure, activity, posture,respiration, thoracic impedance, impedance trend, risk of hypervolemiaor hypovolemia etc. Evaluation period start and end times are alsostored in memory. Heart failure patient data includes data observations(e.g. data sensed from sensors that cross a threshold). Additionally,evaluation period data is also stored in memory 108 a-c. For example,the start and end dates of the evaluation period data is stored inmemory 108 a-c.

Programmer 24 can include any appropriate programming system, includingone generally known to those skilled in the art, such as the MedtronicCARELINK™ programmer, sold by Medtronic, Plc. of Minneapolis, Minn.Programmer 24 may communicate wirelessly with IMD 16, such as using RFcommunication or proximal inductive interaction. This wirelesscommunication is possible through the use of telemetry module, which maybe coupled to an internal antenna or an external antenna. An externalantenna that is coupled to programmer 24 may correspond to theprogramming head that may be placed over heart. The telemetry module mayalso be configured to communicate with another computing device viawireless communication techniques, or direct communication through awired connection. Examples of local wireless communication techniquesthat may be employed to facilitate communication between programmer 24and another computing device include RF communication according to the102.11 or Bluetooth specification sets, infrared communication, e.g.,according to the IrDA standard, or other standard or proprietarytelemetry protocols. In this manner, other external devices may becapable of communicating with programmer 24 without needing to establisha secure wireless connection. An additional computing device incommunication with programmer 24 may be a networked device such as aserver capable of processing information retrieved from IMD 16.

In this manner, programmer telemetry module (not shown) may transmit aninterrogation request to telemetry module of IMD 16. Accordingly, thetelemetry module may receive data (e.g. diagnostic information,real-time data related to absolute intrathoracic impedance that may beindicative of hypervolemia or hypovolemia, etc.) or diagnosticinformation selected by the request or based on already entered patientstatus to IMD 16. The data may include patient metric values or otherdetailed information from telemetry module of IMD 16. The data mayinclude an alert or notification of the heart failure risk level fromthe telemetry module of IMD 16. The alert may be automaticallytransmitted, or pushed, by IMD 16 when the heart failure risk levelbecomes critical. In addition, the alert may be a notification to ahealthcare professional, e.g., a clinician or nurse, of the risk leveland/or an instruction to patient 14 to seek medical treatment (e.g.testing to confirm worsening HF etc.). In response to receiving thealert, the user interface may display the alert to the healthcareprofessional regarding the risk level or present an instruction topatient 14 to seek medical treatment.

Either in response to heart failure data, e.g., the risk level orpatient metrics, or requested heart failure information, the userinterface for a computing device or programmer 24 may present thepatient metrics, the heart failure risk level, or recommended treatment(e.g. medication) to the user. In some examples, the user interface mayalso highlight each of the patient metrics that have exceeded therespective one of the plurality of metric-specific thresholds. In thismanner, the user may quickly review those patient metrics that havecontributed to the identified heart failure risk level.

Access point 110 may comprise a device that connects to network 112 viaany of a variety of connections, such as telephone dial-up, digitalsubscriber line (DSL), or cable modem connections. In other examples,access point 110 may be coupled to network 112 through different formsof connections, including wired or wireless connections. In someexamples, access point 110 may be co-located with patient 14 and maycomprise one or more programming units and/or computing devices (e.g.,one or more monitoring units) that may perform various functions andoperations described herein.

In another example, access point 110 may be a LINQ™ device co-locatedwithin the patient and configured to sense, record and transmit data tonetwork 110. Alternatively, SEEQ™, configured for monitoring, maybeattached to the skin of the patient. For example, SEEQ™ could beattached to the skin over the heart of the patient for cardiacmonitoring. In another example, access point 110 may include ahome-monitoring unit that is located within patient 14 and that maymonitor the activity of IMD 16. LINQ™ and SEEQ™ commercially availablefrom Medtronic, Inc. located in Minneapolis, Minn. may also be used asaccess point 110. An example of such a LINQ™ may be seen with respect toU.S. Pregrant Publication No. 2016-0310031 A1 filed Apr. 20, 2016, andassigned to the assignee of the present invention, the disclosure ofwhich is incorporated by reference in its entirety herein.

Server 130 can be located at a centralized communication center such asat Cardiocom®. Server 130 is configured to perform complex computationsfor a large group of patients and provides secure storage in memory 136for archival of information (e.g., patient metric data, heart failurerisk levels, weight, blood pressure etc.) setup in a database 132 thathas been collected and generated from IMD 16, programmer 24 and/orexternal devices. Exemplary medium and high risk calculations performedby server 130 (or any processor of a computing device) are shown anddescribed in US 2016-0361026 A1 (U.S. application Ser. No. 13/391,376)entitled METHOD AND APPARATUS FOR MONITORING TISSUE FLUID CONTENT FORUSE IN AN IMPLANTABLE CARDIAC DEVICE and US2012032243 (U.S. applicationSer. No. 12/914,836 filed Oct. 28, 2010), entitled HEART FAILUREMONITORING AND NOTIFICATION, U.S. Provisional Application No. 62/554,523filed Sep. 7, 2017 and entitled, DIFFERENTIATION OF HEART FAILURE RISKSCORES FOR HEART FAILURE MONITORING, and assigned to the assignee of thepresent invention, the disclosure of which is incorporated by referencein its entirety herein.

Examples of medium and high risk status are presented in FIGS. 7-8.Medium risk status, for example, may involve one or more conditions suchas AT/AF burden exceeding a threshold value (>6 hours/day), low % Vpacing and high night heart rate (>85 bpm). High risk status, forexample, may involve one or more conditions such as highOptiVol™/impedance index (>60 ohm-days), patient activity (<1 hour/day),high night heart rate (>85 bpm) and low HRV (<60 ms).

Memory 136 stores a set of diagnostic metrics indicative of worseningheart failure for each patient. Diagnostic metrics or metrics caninclude a variety of data. Exemplary data, shown in FIG. 5, includes (1)impedance trend index commercially available in IMDs from MedtronicPlc., located in MN), (2) intrathoracic impedance, (3) atrialtachycardia/atrial fibrillation (AT/AF) burden, (4) mean ventricularrate during AT/AF, (5) patient activity, (6) ventricular (V) rate, (7)day and night heart rate, (8) percent CRT pacing, and/or (9) number ofshocks. The impedance index is an indicator of the amount of fluidcongestion experienced by the patient. The impedance index is thedifference between an impedance measured during real time using IMD 16and a reference impedance, that can be continuously updated, establishedby the IMD 16 or during another visit to the physician. The impedanceindex is described in greater detail with respect to U.S. patent Ser.No. 10/727,008 filed on Dec. 3, 2003 issued as U.S. Pat. No. 7,986,994,and assigned to the assignee of the present invention, the disclosure ofwhich is incorporated by reference in its entirety herein.

Heart rate variability (HRV) is a marker of autonomic tone and has beenshown to provide prognostic information for mortality risk. A decreasein HRV is associated with increased sympathetic tone. Using HRV devicediagnostic data, patients with low HRV (<100 ms) are at a highercombined risk of death and hospitalization. Patients with HRV <50 msexhibit an even higher risk than those with HRV in the range of 50-100ms.

Similar to HRV, elevated heart rate is a marker of elevated sympathetictone and has been shown to have prognostic value for worsening HF. NightHeart Rate (NHR), measured between midnight and 4 AM, can be a bettermetric than the day time heart rate. Day time heart rate can be affectedby varying activity level (e.g. rest and exercise). Patients with highNHR (75±25 bpm) typically experience higher risk of being hospitalizedor dying than those who had low NHR (73±11 bpm).

Additionally, declining patient activity is associated with worsening HFstatus and can potentially be of value for predicting HFhospitalization. Declining patient activity can be determined by avariety of activity devices such as a FITBIT, cellphone etc.

Combination variables (e.g. combining pacing and arrhythmia relatedinformation) can also be used to evaluate worsening HF risk. Forexample, one of the components of combination variable is substantialdecrease (>8%) in CRT pacing, which is associated with high HF events. Adecline in CRT pacing can occur because of rapid conduction during AF.Thus, mean ventricular rate ≥90 bpm and atrial fibrillation (AF) burden≥6 hours/day and shocks delivered to Ventricular FibrillationNentricularTachycardia (VT/VF) can also be components of the combination variable.

IMD 16, programmer 24, and/or computing devices a-n may communicate viawireless communication using any techniques known in the art. Examplesof communication techniques may include, for example, radiofrequency(RF) telemetry, but other communication techniques such as magneticcoupling are also contemplated. In some examples, programmer 24 mayinclude a programming head that may be placed proximate to the body ofthe patient near the IMD 16 implant site in order to improve the qualityor security of communication between IMD 16 and programmer 24.

Network 110 may comprise a local area network, wide area network, orglobal network, such as the Internet. In some cases, programmer 24 orexternal server 130 may assemble the diagnostic data, heart failuredata, prospective heart failure risk data or other suitable data in webpages or other documents for viewing by and trained professionals, suchas clinicians, via viewing terminals associated with computing devices120. The system 100 of FIG. 1 may be implemented, in some aspects, withgeneral network technology and functionality similar to that provided bythe Medtronic CareLink® Network developed by Medtronic, Plc., ofMinneapolis, Minn.

FIG. 2 is an enlarged view of IMD 16. IMD can be a leadless pacingdevice such as MICRA™ that is commercially available from Medtronic,Inc. located in Minneapolis, Minn. IMD 16 can also be a pacemaker (orICD) coupled to leads 18, 20, and 22 and programmer 24. IMD 16 may be,for example, an implantable pacemaker, cardioverter, and/ordefibrillator that provides electrical signals to heart 12 viaelectrodes coupled to one or more of leads 18, 20, and 22. Patient 14 isordinarily, but not necessarily a human patient. In general, thetechniques described in this disclosure may be implemented by anymedical device, e.g., implantable or external, that senses a signalindicative of cardiac activity, patient 14 activity, and/or fluid volumewithin patient 14. As one alternative example, the techniques describedherein may be implemented in an external cardiac monitor that generateselectrograms of heart 12 and detects thoracic fluid volumes,respiration, and/or cardiovascular pressure of patient 14.

Leads 18, 20, 22 extend into the heart 12 of patient 14 to senseelectrical activity of heart 12 and/or deliver electrical stimulation toheart 12. Leads 18, 20, and 22 may also be used to detect a thoracicimpedance indicative of fluid volume in patient 14, respiration rates,sleep apnea, or other patient metrics. Respiration metrics, e.g.,respiration rates, tidal volume, and sleep apnea, may also be detectablevia an electrogram, e.g., based on a signal component in a cardiacelectrogram that is associated with respiration. In the example shown inFIG. 1, right ventricular (RV) lead 18 extends through one or more veins(not shown), the superior vena cava (not shown), and right atrium 26,and into right ventricle 28. Left ventricular (LV) coronary sinus lead20 extends through one or more veins, the vena cava, right atrium 26,and into the coronary sinus 30 to a region adjacent to the free wall ofleft ventricle 32 of heart 12. Right atrial (RA) lead 22 extends throughone or more veins and the vena cava, and into the right atrium 26 ofheart 12.

In some examples, system 100 may additionally or alternatively includeone or more leads or lead segments (not shown in FIG. 2) that deploy oneor more electrodes within the vena cava, or other veins. Furthermore, insome examples, system 100 may additionally or alternatively includetemporary or permanent epicardial or subcutaneous leads with electrodesimplanted outside of heart 12, instead of or in addition to transvenous,intracardiac leads 18, 20 and 22. Such leads may be used for one or moreof cardiac sensing, pacing, or cardioversion/defibrillation. Forexample, these electrodes may allow alternative electrical sensingconfigurations that provide improved or supplemental sensing in somepatients. In other examples, these other leads may be used to detectintrathoracic impedance as a patient metric for identifying a heartfailure risk or fluid retention levels.

IMD 16 may sense electrical signals attendant to the depolarization andrepolarization of heart 12 via electrodes (not shown in FIG. 1) coupledto at least one of the leads 18, 20, 22. In some examples, IMD 16provides pacing pulses to heart 12 based on the electrical signalssensed within heart 12. The configurations of electrodes used by IMD 16for sensing and pacing may be unipolar or bipolar. IMD 16 may detectarrhythmia of heart 12, such as tachycardia or fibrillation of the atria26 and 36 and/or ventricles 28 and 32, and may also providedefibrillation therapy and/or cardioversion therapy via electrodeslocated on at least one of the leads 18, 20, 22. In some examples, IMD16 may be programmed to deliver a progression of therapies, e.g., pulseswith increasing energy levels, until a fibrillation of heart 12 isstopped. IMD 16 may detect fibrillation employing one or morefibrillation detection techniques known in the art.

In addition, IMD 16 may monitor the electrical signals of heart 12 forpatient metrics stored in IMD 16 and/or used in generating the heartfailure risk level. IMD 16 may utilize two of any electrodes carried onleads 18, 20, 22 to generate electrograms of cardiac activity. In someexamples, IMD 16 may also use a housing electrode of IMD 16 (not shown)to generate electrograms and monitor cardiac activity. Although theseelectrograms may be used to monitor heart 12 for potential arrhythmiasand other disorders for therapy, the electrograms may also be used tomonitor the condition of heart 12. For example, IMD 16 may monitor heartrate (night time and day time), heart rate variability, ventricular oratrial intrinsic pacing rates, indicators of blood flow, or otherindicators of the ability of heart 12 to pump blood or the progressionof heart failure.

In some examples, IMD 16 may also use any two electrodes of leads 18,20, and 22 or the housing electrode to sense the intrathoracic impedanceof patient 14. As the tissues within the thoracic cavity of patient 14increase in fluid content, the impedance between two electrodes may alsochange. For example, the impedance between an RV coil electrode and thehousing electrode may be used to monitor changing intrathoracicimpedance.

IMD 16 may use intrathoracic impedance to create a fluid index. As thefluid index increases, more fluid is being retained within patient 14and heart 12 may be stressed to keep up with moving the greater amountof fluid. Therefore, this fluid index may be a patient metrictransmitted in diagnostic data or used to generate the heart failurerisk level. By monitoring the fluid index in addition to other patientmetrics, IMD 16 may be able to reduce the number of false positive heartfailure identifications relative to what might occur when monitoringonly one or two patient metrics. Furthermore, IMD 16, along with othernetworked computing devices described herein, may facilitate remotemonitoring of patient 14, e.g., monitoring by a health care professionalwhen the patient is not located in a healthcare facility or clinicassociated with the health care professional, during apost-hospitalization period. An example system for measuring thoracicimpedance and determining a fluid index is described in U.S. Pat. No.8,255,046 to Sarkar et al., entitled, “DETECTING WORSENING HEART FAILUREBASED ON IMPEDANCE MEASUREMENTS,” which published on Feb. 4, 2010 and isincorporated herein by reference in its entirety.

Whether a patient begins to experience or is experiencing HF symptoms isbased upon a variety of parameters that can change over time. Exemplaryparameters capable of changing over time includes the patient's weight(i.e. extreme weight loss), hypotension, syncope, pre-syncope, all ofwhich can be uploaded to the system 100 on a periodic basis (e.g. daily,weekly, monthly etc.) from the patient's computer and/or user device 102a-n.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of IMD 16. In the illustrated example, IMD 16 includes aprocessor 80, memory 82, metric detection module 92, signal generator84, sensing module 86, telemetry module 88, and power source 90. Memory82 includes computer-readable instructions that, when executed byprocessor 80, cause IMD 16 and processor 80 to perform various functionsattributed to IMD 16 and processor 80 herein. Memory 82 may include anyvolatile, non-volatile, magnetic, optical, or electrical media, such asa random access memory (RAM), read-only memory (ROM), non-volatile RAM(NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory,or any other digital or analog media.

Processor 80 may include any one or more of a microprocessor, acontroller, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), orequivalent discrete or analog logic circuitry. In some examples,processor 80 may include multiple components, such as any combination ofone or more microprocessors, one or more controllers, one or more DSPs,one or more ASICs, or one or more FPGAs, as well as other discrete orintegrated logic circuitry. The functions attributed to processor 80herein may be embodied as software, firmware, hardware or anycombination thereof.

Processor 80 controls signal generator 84 to deliver stimulation therapyto heart 12 according to a therapy parameters, which may be stored inmemory 82. For example, processor 80 may control signal generator 84 todeliver electrical pulses with the amplitudes, pulse widths, frequency,or electrode polarities specified by the therapy parameters.

Signal generator 84 is electrically coupled to electrodes 40, 42, 44,46, 48, 50, 58, 62, 64, and 66, e.g., via conductors of the respectivelead 18, 20, 22, or, in the case of housing electrode 58, via anelectrical conductor disposed within housing 60 of IMD 16. In theillustrated example, signal generator 84 is configured to generate anddeliver electrical stimulation therapy to heart 12. For example, signalgenerator 84 may deliver defibrillation shocks to heart 12 via at leasttwo electrodes 58, 62, 64, 66. Signal generator 84 may deliver pacingpulses via ring electrodes 40, 44, 48 coupled to leads 18, 20, and 22,respectively, and/or helical electrodes 42, 46, and 50 of leads 18, 20,and 22, respectively. In some examples, signal generator 84 deliverspacing, cardioversion, or defibrillation stimulation in the form ofelectrical pulses. In other examples, signal generator may deliver oneor more of these types of stimulation in the form of other signals, suchas sine waves, square waves, or other substantially continuous timesignals.

Signal generator 84 may include a switch module and processor 80 may usethe switch module to select, e.g., via a data/address bus, which of theavailable electrodes are used to deliver defibrillation pulses or pacingpulses. The switch module may include a switch array, switch matrix,multiplexer, or any other type of switching device suitable toselectively couple stimulation energy to selected electrodes.

Electrical sensing module 86 monitors signals from at least one ofelectrodes 40, 42, 44, 46, 48, 50, 58, 62, 64 or 66 in order to monitorelectrical activity of heart 12, impedance, or other electricalphenomenon. Sensing may be done to determine heart rates or heart ratevariability, or to detect arrhythmias or other electrical signals.Sensing module 86 may also include a switch module to select which ofthe available electrodes are used to sense the heart activity, dependingupon which electrode combination, or electrode vector, is used in thecurrent sensing configuration. In some examples, processor 80 may selectthe electrodes that function as sense electrodes, i.e., select thesensing configuration, via the switch module within sensing module 86.Sensing module 86 may include one or more detection channels, each ofwhich may be coupled to a selected electrode configuration for detectionof cardiac signals via that electrode configuration. Some detectionchannels may be configured to detect cardiac events, such as P- orR-waves, and provide indications of the occurrences of such events toprocessor 80, e.g., as described in U.S. Pat. No. 5,117,824 to Keimel etal., which issued on Jun. 2, 1992 and is entitled, “APPARATUS FORMONITORING ELECTRICAL PHYSIOLOGIC SIGNALS,” and is incorporated hereinby reference in its entirety. Processor 80 may control the functionalityof sensing module 86 by providing signals via a data/address bus.

Processor 80 may include a timing and control module, which may beembodied as hardware, firmware, software, or any combination thereof.The timing and control module may comprise a dedicated hardware circuit,such as an ASIC, separate from other processor 80 components, such as amicroprocessor, or a software module executed by a component ofprocessor 80, which may be a microprocessor or ASIC. The timing andcontrol module may implement programmable counters. If IMD 16 isconfigured to generate and deliver pacing pulses to heart 12, suchcounters may control the basic time intervals associated with DDD, WI,DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR, CRT, and othermodes of pacing.

Intervals defined by the timing and control module within processor 80may include atrial and ventricular pacing escape intervals, refractoryperiods during which sensed P-waves and R-waves are ineffective torestart timing of the escape intervals, and the pulse widths of thepacing pulses. As another example, the timing and control module maywithhold sensing from one or more channels of sensing module 86 for atime interval during and after delivery of electrical stimulation toheart 12. The durations of these intervals may be determined byprocessor 80 in response to stored data in memory 82. The timing andcontrol module of processor 80 may also determine the amplitude of thecardiac pacing pulses.

Interval counters implemented by the timing and control module ofprocessor 80 may be reset upon sensing of R-waves and P-waves withdetection channels of sensing module 86. In examples in which IMD 16provides pacing, signal generator 84 may include pacer output circuitsthat are coupled, e.g., selectively by a switching module, to anycombination of electrodes 40, 42, 44, 46, 48, 50, 58, 62, or 66appropriate for delivery of a bipolar or unipolar pacing pulse to one ofthe chambers of heart 12. In such examples, processor 80 may reset theinterval counters upon the generation of pacing pulses by signalgenerator 84, and thereby control the basic timing of cardiac pacingfunctions, including anti-tachyarrhythmia pacing.

The value of the count present in the interval counters when reset bysensed R-waves and P-waves may be used by processor 80 to measure thedurations of R-R intervals, P-P intervals, P-R intervals and R-Pintervals, which are measurements that may be stored in memory 82.Processor 80 may use the count in the interval counters to detect atachyarrhythmia event, such as atrial fibrillation (AF), atrialtachycardia (AT), ventricular fibrillation (VF), or ventriculartachycardia (VT). These intervals may also be used to detect the overallheart rate, ventricular contraction rate, and heart rate variability. Aportion of memory 82 may be configured as a plurality of recirculatingbuffers, capable of holding series of measured intervals, which may beanalyzed by processor 80 in response to the occurrence of a pace orsense interrupt to determine whether the patient's heart 12 is presentlyexhibiting atrial or ventricular tachyarrhythmia.

In some examples, an arrhythmia detection method may include anysuitable tachyarrhythmia detection algorithms. In one example, processor80 may utilize all or a subset of the rule-based detection methodsdescribed in U.S. Pat. No. 5,545,186 to Olson et al., entitled,“PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENTOF ARRHYTHMIAS,” which issued on Aug. 13, 1996, or in U.S. Pat. No.5,755,736 to Gillberg et al., entitled, “PRIORITIZED RULE BASED METHODAND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS,” which issuedon May 26, 1998. U.S. Pat. No. 5,545,186 to Olson et al. U.S. Pat. No.5,755,736 to Gillberg et al. is incorporated herein by reference intheir entireties. However, other arrhythmia detection methodologies mayalso be employed by processor 80 in other examples.

In some examples, processor 80 may determine that tachyarrhythmia hasoccurred by identification of shortened R-R (or P-P) interval lengths.Generally, processor 80 detects tachycardia when the interval lengthfalls below 220 milliseconds (ms) and fibrillation when the intervallength falls below 180 ms. These interval lengths are merely examples,and a user may define the interval lengths as desired, which may then bestored within memory 82. This interval length may need to be detectedfor a certain number of consecutive cycles, for a certain percentage ofcycles within a running window, or a running average for a certainnumber of cardiac cycles, as examples.

In the event that processor 80 detects an atrial or ventriculartachyarrhythmia based on signals from sensing module 86, and ananti-tachyarrhythmia pacing regimen is desired, timing intervals forcontrolling the generation of anti-tachyarrhythmia pacing therapies bysignal generator 84 may be loaded by processor 80 into the timing andcontrol module to control the operation of the escape interval counterstherein and to define refractory periods during which detection ofR-waves and P-waves is ineffective to restart the escape intervalcounters for the an anti-tachyarrhythmia pacing. Processor 80 detectsdata (e.g. data observations etc.) at an IMD16 check and/orinterrogation time point. Data is sensed based on signals from sensingmodule 86. Additionally, cardioversion or defibrillation shock can bedetermined to be needed based upon sensed data, and processor 80 maycontrol the amplitude, form and timing of the shock delivered by signalgenerator 84.

Memory 82 is configured to store data. Exemplary data can be associatedwith a variety of operational parameters, therapy parameters, sensed anddetected data, and any other information related to the therapy andtreatment of patient 14. In the example of FIG. 3, memory 82 alsoincludes metric parameters 83 and metric data 85. Metric parameters 83may include all of the parameters and instructions required by processor80 and metric detection module 92 to sense and detect each of thepatient metrics used to generate the diagnostic information transmittedby IMD 16. Metric data 85 may store all of the data generated from thesensing and detecting of each patient metric. In this manner, memory 82stores a plurality of automatically detected patient metrics as the datarequired to generate a risk level of patient 14 being admitted to thehospital due to heart failure.

Metric parameters 83 may include definitions of each of the patientmetrics automatically sensed or measured by metric detection module 92.These definitions may include instructions regarding what electrodes orsensors to use in the detection of each metric. Preferred metricsinclude an (1) impedance trend index (also referred to as OPTIVOL®commercially available in IMDs from Medtronic Inc., located in MN), (2)intrathoracic impedance, (3) atrial tachycardia/atrial fibrillation(AT/AF) burden, (4) mean ventricular rate during AT/AF, (5) patientactivity, (6) V rate, (7) day and night heart rate, (8) percent CRTpacing, and/or (9) number of shocks. Impedance trend index is describedwith respect to U.S. patent Ser. No. 10/727,008 filed on Dec. 3, 2003issued as U.S. Pat. No. 7,986,994, and assigned to the assignee of thepresent invention, the disclosure of which is incorporated by referencein its entirety herein. Other suitable metrics can also be used. Forexample, a reference or baseline level impedance is established for apatient from which subsequently acquired raw impedance data is compared.For example, raw impedance can be acquired from the electrodes (e.g. RVcoil to Can) and compared to the reference impedance. Baseline impedancecan be derived by averaging impedance over a duration of 7 days (1-week)to 90 days (3-months).

Metric parameters 83 may also store a metric-specific threshold for eachof the patient metrics automatically detected by metric detection module92. Metric thresholds may be predetermined and held constant over theentire monitoring of patient 14. In some examples, however, metricthresholds may be modified by a user during therapy or processor 80 mayautomatically modify one or more metric thresholds to compensate forcertain patient conditions. For example, a heart rate threshold may bechanged over the course of monitoring if the normal or baseline heartrate has changed during therapy.

In one example, these metric-specific thresholds may include a thoracicfluid index threshold of about 60 Ω-days an atrial fibrillation burdenthreshold of approximately 6 consecutive hours, a ventricularcontraction rate threshold approximately equal to 90 beats per minutefor 24 hours, a patient activity threshold approximately equal to 1 hourper day for seven consecutive days, a nighttime heart rate threshold ofapproximately 85 beats per minute for seven consecutive days, a heartrate variability threshold of approximately 40 milliseconds for sevenconsecutive days, a cardiac resynchronization therapy percentagethreshold of 90 percent for five of seven consecutive days, and anelectrical shock number threshold of 1 electrical shock. Thesethresholds may be different in other examples, and may be configured bya user, e.g., a clinician, for an individual patient.

Processor 80 may alter the method with which patient metrics are storedin memory 82 as metric data 85. In other words, processor 80 may storethe automatically detected patient metrics with a dynamic data storagerate.

Metric data 85 is a portion of memory 82 that may store some or all ofthe patient metric data that is sensed and/or detected by metricdetection module 92. Metric data 85 may store the data for each metricon a rolling basis during an evaluation window. The evaluation windowmay only retain recent data and delete older data from the evaluationwindow when new data enters the evaluation window. In this manner, theevaluation window may include only recent data for a predeterminedperiod of time. In one or more other embodiments, memory can beconfigured for long term storage of data. Processor 80 may access metricdata when necessary to retrieve and transmit patient metric data and/orgenerate heart failure risk levels. In addition, metric data 85 maystore any and all data observations, heart failure risk levels or othergenerated information related to the heart failure risk of patient 14.The data stored in metric data 85 may be transmitted as part ofdiagnostic information. Although metric parameters 83 and/or metric data85 may consist of separate physical memories, these components maysimply be an allocated portion of the greater memory 82.

Metric detection module 92 may automatically sense and detect each ofthe patient metrics. Metric detection module 92 may then generatediagnostic data, e.g., data that indicates a threshold has been crossed,risk levels, based on the patient metrics. For example, metric detectionmodule 92 may measure the thoracic impedance, analyze an electrogram ofheart 12, monitor the electrical stimulation therapy delivered topatient 14, or sense the patient activity. It is noted that functionsattributed to metric detection module 92 herein may be embodied assoftware, firmware, hardware or any combination thereof. In someexamples, metric detection module 92 may at least partially be asoftware process executed by processor 80. Metric detection module 92may sense or detect any of the patient metrics used as a basis forgenerating the heart failure risk level or otherwise indication of heartfailure status or that patient 14 is at risk for worsening HF. In oneexample, metric detection module 92 may compare each of the patientmetrics to their respective metric-specific thresholds defined in metricparameters 83 to generate the heart failure risk level. Metric detectionmodule 92 may automatically detect two or more patient metrics. In otherexamples, metric detection module 92 may detect different patientmetrics.

In one example, metric detection module 92 may analyze electrogramsreceived from sensing module 86 to detect an atrial fibrillation oratrial tachycardia, and determine atrial tachycardia or fibrillationburden, e.g., duration, as well as a ventricular contraction rate duringatrial fibrillation. Metric detection module 92 may also analyzeelectrograms in conjunction with a real-time clock, patient posture oractivity signal, e.g., from activity sensor 96, and/or otherphysiological signals indicative of when a patient is asleep or awake todetermine a nighttime (or sleeping) heart rate or a daytime (or awake)heart rate or a difference between the day and night heart rate, andalso analyze electrograms to determine a heart rate variability, or anyother detectable cardiac events from one or more electrograms. Asdescribed above, metric detection module 92 may use peak detection,interval detection, or other methods to analyze the electrograms.

In addition, metric detection module 92 may include and/or controlimpedance module 94 and activity sensor 96. Impedance module 94 may beused to detect the thoracic impedance used to generate the thoracicfluid index. As described herein, impedance module 94 may utilize any ofthe electrodes of disclosed herein to take intrathoracic impedancemeasurements. In other examples, impedance module 94 may utilizeseparate electrodes coupled to IMD 16 or in wireless communication withtelemetry module 88. Once impedance module 94 measures the intrathoracicimpedance of patient 14, metric detection module 92 may generate thethoracic fluid index and compare the index to the thoracic fluid indexthreshold defined in metric parameters 83.

Activity sensor 96 may include one or more accelerometers or otherdevices capable of detecting motion and/or position of patient 14.Activity sensor 96 may therefore detect activities of patient 14 orpostures engaged by patient 14. Metric detection module 92 may, forexample, monitor the patient activity metric based on the magnitude orduration of each activity and compare the determined metric data to theactivity threshold defined in metric parameters 83. In addition todetecting events of patient 14, metric detection module 92 may alsodetect certain therapies delivered by signal generator 84, e.g., asdirected by processor 80. Metric detection module 92 may monitor signalsthrough signal generator 84 or receive therapy information directly fromprocessor 80 for the detection. Example patient metrics detected by thismethod may include a cardiac resynchronization therapy percentage ormetrics related to delivery of electrical shocks.

The cardiac resynchronization therapy (CRT) metric may be the amount orpercentage of time each day, or an amount of percentage of cardiaccycles, as examples, that IMD 16 delivers cardiac resynchronizationtherapy to heart 12. Low CRT amounts or percentages may indicate thatbeneficial therapy is not being effectively delivered and thatadjustment of therapy parameters, e.g., an atrioventricular delay or alower pacing rate, may improve therapy efficacy. In one example, higherCRT amounts or percentages may indicate that heart 12 is sufficientlypumping blood through the vasculature with the aid of therapy to preventfluid buildup. In examples of other types of cardiac pacing (non-CRT) orstimulation therapy, higher therapy percentages may indicate that heart12 is unable to keep up with blood flow requirements. In one or moreother embodiments, low effective CRT amounts or effective V-pacing forCRT pacing can also be used as indicators of improved therapy efficacy.

An electrical shock may be a defibrillation event or other high energyshock used to return heart 12 to a normal rhythm. The metric relatedelectrical shocks may be a number or frequency of electrical shocks,e.g., a number of shocks within a period of time. Metric detectionmodule 92 may detect these patient metrics as well and compare them to acardiac resynchronization therapy percentage and shock event threshold,respectively, defined in metric parameters 83 to determine when eachpatient metric has become critical. In one example, the electrical shockevent metric may become critical when a threshold number of shocks isdelivered, e.g., within a time period, or even when patient 14 evenreceives one therapeutic shock.

Metric detection module 92 may include additional sub-modules orsub-routines that detect and monitor other patient metrics used tomonitor patient 14 and/or generate the HF risk level. In some examples,metric detection module 92, or portions thereof, may be incorporatedinto processor 80 or sensing module 86. In other examples, raw data usedto produce patient metric data may be stored in metric data 85 for laterprocessing or transmission to an external device. An external device maythen produce each patient metric from the raw data, e.g., electrogram orraw intrathoracic impedance which is subsequently compared to areference impedance. In other examples, metric detection module 92 mayadditionally receive data from one or more implanted or external devicesused to detect each metric which IMD 16 may store as metric data.

In some examples, the patient metric thresholds used to generate therisk levels may change over time, e.g., the patient metric thresholdsmay either be modified by a user or automatically changed based on otherpatient conditions. Telemetry module 88 may receive commands fromprogrammer 24, for example, to modify one or more metric parameters 83(e.g., metric creation instructions or metric-specific thresholds). Insome examples, processor 80 may automatically adjust a metric-specificthreshold if certain conditions are present in patient 14. For example,the threshold may be adjusted if patient 14 is experiencing certainarrhythmias or data contained in cardiac electrograms change, e.g.,there is a deviation in ST elevations or presence of pre-ventricularcontractions, in such a manner that requires a change in the threshold.

Processor 80 may generate risk levels (e.g. risk of, or exhibitinghypervolemia, hypovolemia, HFH risk level) based upon the patientmetrics sensed, detected, and stored in metric data 85 of memory 82. Forexample, processor 80 may continually update the risk level as metricdetection module 92 updates each patient metric. In other examples,processor 80 may periodically update the HFH risk level according to anupdating schedule. In one or more other embodiments, the total number ofdata observations that exceed or cross a threshold within apre-specified period of time can be used to determine the risk of aheart failure event or worsening HF.

As described above, processor 80 may provide an alert to a user, e.g.,of programmer 24, regarding the data from any patient metric and/or theHFH risk level. In one example, processor 80 may provide an alert withthe HFH risk level when programmer 24 or another device communicateswith IMD 16. Telemetry module 88 includes any suitable hardware,firmware, software or any combination thereof for communicating withanother device, such as programmer 24 (FIG. 1). Under the control ofprocessor 80, telemetry module 88 may receive downlink telemetry fromand send uplink telemetry to programmer 24 with the aid of an antenna,which may be internal and/or external. Processor 80 may provide the datato be uplinked to programmer 24 and the control signals for thetelemetry circuit within telemetry module 88, e.g., via an address/databus. In some examples, telemetry module 88 may provide received data toprocessor 80 via a multiplexer.

In some examples, processor 80 may transmit atrial and ventricular heartsignals, e.g., EGMs, produced by atrial and ventricular sense amplifiercircuits within sensing module 86 to programmer 24. Programmer 24 mayinterrogate IMD 16 to receive the heart signals. Processor 80 may storeheart signals within memory 82, and retrieve stored heart signals frommemory 82. Processor 80 may also generate and store marker codesindicative of different cardiac events that sensing module 86 detects,and transmit the marker codes to programmer 24. An example pacemakerwith marker-channel capability is described in U.S. Pat. No. 4,374,382to Markowitz, entitled, “MARKER CHANNEL TELEMETRY SYSTEM FOR A MEDICALDEVICE,” which issued on Feb. 15, 1983 and is incorporated herein byreference in its entirety.

In some examples, IMD 16 may signal programmer 24 to further communicatewith and pass the alert through a network such as the MedtronicCareLink® Network developed by Medtronic, Plc. of Minneapolis, Minn., orsome other network linking patient 14 to a clinician. In this manner, acomputing device or user interface of the network may be the externalcomputing device that delivers the alert, e.g., patient metric data. Inother examples, one or more steps in the generation of the heart failurerisk level may occur within a device external of patient 14, e.g.,within programmer 24 ora server networked to programmer 24. In thismanner, IMD 16 may detect and store patient metrics before transmittingthe patient metrics to a different computing device.

System 100 controls implementation of an intervention method 200,depicted in a flow diagram of FIG. 4, to seamlessly adjust patient'stherapy (e.g. medication). At block 202, a determination is made as towhether the patient is experiencing increased risk of worsening HFcondition. Risk of worsening HF condition is calculated using data suchas data acquired from IMD 16. For example, data, acquired from the IMD16, shows a threshold level is crossed. The data, showing an exceedanceor that a threshold has been crossed, is transmitted to server 130.Other data that may be useful for determining risk of worseningcondition can be obtained from computing devices 102 a-n.

Server 130 combines all of the diagnostic data in order to determine apatient's HF risk. Numerous methods exist for determining a patient'srisk of experiencing a HF event. One methodology uses a Bayesian BeliefProbabilistic model to categorize patients into three riskcategories—low, medium and high. Exemplary medium and high riskcalculations are shown and described in US2012032243, entitled HEARTFAILURE MONITORING AND NOTIFICATION and assigned to the assignee of thepresent invention, the disclosure of which is incorporated by referencein its entirety herein. One or more other embodiments that may beemployed is directed to Martin R. Cowie et al., Development andValidation Of An Integrated Diagnostic Algorithm Derived From ParametersMonitored in Implantable Devices For Identifying Patients At Risk ForHeart Failure Hospitalization In An Ambulatory Setting, European HeartJournal (2013) 34, 2472-2480 doi:10.1093/eurheartj/eht083, thedisclosure of which is incorporated by reference in its entirety herein.Another exemplary method and system is described in U.S. ProvisionalApplication No. 62/554,523 filed Sep. 7, 2017, entitled, DIFFERENTIATIONOF HEART FAILURE RISK SCORES FOR HEART FAILURE MONITORING, and assignedto the assignee of the present invention, the disclosure of which isincorporated by reference in its entirety herein. HF risk scorealgorithms may use data, acquired from signals sensed from electrodesthat are associated with an implantable medical device and/or electrodeslocated on an external surface (e.g. skin) of the patient.

FIG. 10 depicts a method 300 of using a system for managing patienttherapy. The system 100 comprises an implantable medical device 16 (e.g.pacemaker, ICD, implantable monitoring device e.g. LINQ™ etc.)comprising one or more electrodes configured to be implanted within apatient's body, to acquire first signals corresponding to signals sensedat block 302 from within the patient's body and to generate first datatransmissions in response to the acquired first signals at block 304. Atblock 306, a wearable device (e.g. watch, SEEQ™ etc.) comprising one ormore electrodes configured to be positioned in contact with an externalsurface of the patient's body, to acquire second signals correspondingto signals sensed from the external surface of the patient's body, andto generate second data transmissions in response to the acquired secondsignals.

At block 308, an input/output device (104 a or at server 130) isconfigured to receive the first data transmissions and the second datatransmissions. At block 310, one or more processors (e.g. server 130,computing device, Iphone™) are configured to receive the first datatransmissions and the second data transmissions. The received first datatransmissions and the received second data transmissions are compared toone or more thresholds that are stored in memory. The one or moreprocessors then compares whether data of the received first datatransmissions and the received second data transmissions is indicativeof a heart failure (HF) worsening episode based on the comparing. Inaddition, the one or more processors send a control signal to theimplantable medical device (e.g. implantable drug dispenser, pacemakeretc.) to adjust a patient's therapy in response to the HF worseningepisode being indicated. Adjusting patient therapy may involve a controlsignal from server 130 or the computing device to the implantablemedical device (e.g. drug delivery, pacemaker etc.) to change a therapyparameter. For example, a dosage can be increased or decreased for adrug. In addition, or alternatively, a pacing parameter can be adjusted.

In one or more embodiments, skilled artisans understand that signalssensed from electrodes located on the skin may not be as accurate assignals that are sensed from electrodes implanted in the body andlocated closer to target tissue. Accordingly, the signals acquired fromelectrodes that are exterior to the patient's body can be weighteddifferently from the signals acquired from electrodes that are implantedin the patient's body. For example, data, from signals sensed fromexterior electrodes, may possess up to 85% quality of data fromimplantable electrodes. Therefore, a correction factor of 0.85 could beapplied to the data from exterior electrodes. Many examples exist ofexterior electrode data and implantable electrode data. For example, aresting heart rate of 45 heart beats per minute (HBM) could be acquiredfrom Garmin™ watch. In contrast, a resting heart rate from IMD 16 couldbe 50 HBM. Since IMD 16 data is presumed to be more accurate thanexterior electrodes placed on the skin, the exterior electrode data canbe adjusted with a correction factor to obtain HR.

An alternative approach involves a computing device, executingalgorithm(s) (e.g. HF risk algorithms etc.), using data solely obtainedfrom exterior electrodes in order to preserve the battery from IMD 16.In this example, the data obtained from exterior electrodes could havebeen previously compared to data obtained from more accurate implantableelectrodes. The difference between the data obtained from IMD 16 andexterior electrodes is considered to be the amount of inaccuracy of theexterior electrodes and implantable electrodes. The data from exteriorelectrodes can be adjusted by using the amount of inaccuracy previouslymeasured. The corrected HBM is as follows:

Resting heart rate from electrodes: [0.85*(45 HBM)+(1.15)*50HBM]/2=47.85 HBM. In another embodiment, data obtained from implantabledevices can be weighted at about 60% while data from wearable devicescan be weighted at about 40%. In yet another embodiment, data obtainedfrom implantable devices can be weighted at about 55% while data fromwearable devices can be weighted at about 45%.

Briefly, the present disclosure uses a set of variables as input.Exemplary set of variables include thoracic impedance, activity, heartrate variability, heart rate, and a combination variable based onarrhythmia and shock related information collected by the IMD 16.Intrathoracic impedance (e.g. OptiVol®) is a useful measure of apatient's HF status because HF status typically worsens when atrialfilling pressure increases thereby causing retention of fluid in thepulmonary circulation. If sustained over time, fluid can infiltrate intointerstitial space leading to worsening pulmonary congestion. Sinceblood and interstitial fluid are highly conductive, fluid accumulationin the pulmonary system leads to a reduction in thoracic impedance.

After a HF risk status of the patient is calculated, the HF risk statusdata is then stored into memory 136 of the server 130. If a patient'srisk is deemed high, the patient automatically falls within the scope ofworsening condition. Worsening HF condition also occurs in medium riskpatients who exhibit sign/symptoms present (e.g. weight gain, dyspneaetc.) that may be acquired from external biometric data devices.

After evaluating patient information, a determination can be made thatthe risk alert from the patient is not specific to worsening HF. In thisscenario, the NO path from block 202 continues from block 206 in whichthe method 200 is terminated and the process returns to monitoring forworsening HF conditions in the patient. The YES path continues fromblock 202 to block 204 in which medical personnel (e.g. nurse located acentral communication center etc.) communicates with the patient throughelectronic communication (e.g. email, text messaging, phone call ormail) in order to determine whether the patient's worsening condition isHF related. The medical personnel may present one or more questions tothe patient. For example, the patient may be asked if he or she hadundergone a recent surgery. At block 208, a determination is made as towhether the threshold crossing is related to HF. A threshold crossingcan be confirmed as a HF occurrence based upon information provided bythe patient. Typically, to confirm whether the worsening condition is HFrelated, the patient is asked to respond to the questions presentedbelow. The questions can be posed by a nurse located near the centralserver 130 or electronically presented to the patient via server 130 toa GUI associated with a computing device 102 a-n. Exemplary questionsthat can be posed to a patient include the following:

1. Has the CRT-D device or lead been changed?

2. Has the patient been discharged from the hospital within the last twodays?

3. Did the patient receive intravenous fluids for more than 1 day whilein the hospital?

4. Did the patient experience chills, shivering, shaking or muscleaches?

5. Has the patient been treated for a chronic obstructive pulmonarydisease (COPD) exacerbation?

6. Did any changes occur to baseline diuretic medication in the past 3weeks?

If the response to anyone of the questions is “yes”, the thresholdcrossing is deemed to not be a HF occurrence. All other occurrences maybe deemed HF related.

If a threshold has been confirmed as having been crossed, the YES pathcontinues to block 210 in which a determination associated with bloodpressure (BP) will require system 100 to intervene by electronicallyindicating that medication should be administered to the patient. BP ofthe patient can be measured relative to a systolic threshold level (TS)and/or a diastolic threshold level (TD). TD and/or TS can be the typicalnormal threshold levels or can be individually established for eachpatient. A determination is made as to whether BP<TS. If BP is greaterthan TS, then the NO path continues to block 206 and the method 200 isterminated and the process returns to monitoring for worsening HFconditions in the patient. In contrast, if BP is greater than or equalto TS, then the NO path from block 210 to block 212 causes a first roundof medication to be provided to the patient. Administration of adiuretic helps to eliminate water and may reduce blood pressure. Toobtain the medication, server 130 is configured to automaticallytransmit a pre-authorized prescription to the patient. Alternatively,the centralized communication center staffed by a registered nursecontacts the patient to indicate that the medication at a certain dosageshould be taken. The prescribed medication is stored in the home of thepatient for easy access. The patient then starts taking the prescribedmedication. In one embodiment, the medication is a diuretic medication(e.g. furosemide) or vasodilator (e.g. nitrate). Diuretics typicallyeliminate water from the patient and reduce the blood pressure.

Another determination is made at block 210 as to whether BP<TD. If BP isless than or equal to TD, then the YES path continues to block 206 andthe process stops and returns to monitoring for worsening HF conditionsin the patient. In contrast, if BP is greater than TD, then the NO pathfrom block 210 to block 212 causes a first round of medication to beprovided to the patient, as described above.

At block 214, a determination is made as to whether the patient isexperiencing hypotension or extreme weight gain in a short period oftime. If the patient is experiencing hypotension, the YES path continuesto block 224 in which another determination is made as to whether thepatient is experiencing HF symptoms. The YES path from block 224continues to block 226 that causes the medication to be stopped orterminated. Medication can be stopped for a variety of conditions.Exemplary conditions include the following:

If the patient weighs less than 150 pounds, and the patient's weightchanges by 3 pounds per 2 days. If the patient weighs between 151-300pounds, and the patient's weight changes by 4 pounds per 2 days.

if the patient weighs greater than 301 pounds, and the patient's weightchanges by 5 pounds per 2 days.

One condition requires both a BP condition and the presence of asymptom, as listed immediately below. The BP condition requires thepatient exhibit either a systolic blood pressure of the patient is lessthan 85 mmHg or a diastolic pressure of less than 40 mmHg. In additionto meeting one of the BP conditions, the patient must be experiencing asymptom that has been conveyed to medical personnel. Exemplary symptomsinclude (1) recent lightheadedness when moving from sitting to standingpositions, or (2) muscle cramping. In addition or alternatively, thephysician may customize any one of these conditions to a patient byadding or reducing the weight gain amount or blood pressure level.

The NO path from blocks 214 and 224 continue to block 216 in which adetermination is made as to whether the patient has recovered from hisor her worsening HF condition. Exemplary criteria for evaluating PRNefficacy in medication intervention is shown in FIG. 6. Recoverycriterion is computed by the server 130 to evaluate PRN efficacy usingraw intrathoracic impedance, acquired from IMD 16 associated with thepatient, since impedance responds dynamically to patient volume status.Computation of recovery criterion requires the difference to becalculated between raw intrathoracic impedance and the referenceimpedance. Reference impedance is a component of impedance trend. Dailyvalues for both raw and reference impedance are included with all devicediagnostic transmissions spanning a duration of up to 14 months. Thedifference between raw and reference impedances on pre-specified timeperiod (e.g. four day time period etc.) is required to compute recoverycriterion (RC)—the day of PRN initiation (x₀), evaluation day (x₃),evaluation Day 1 (x₂), and evaluation Day 2 (x₁), recovery criterion isthen computed according to the following equation:

RC=100*(x ₀ −xa) (+x ₀ −x ₂)+(x ₀ −x ₃))/x ₀.

If the value of RC is greater than a threshold value of 70 (i.e.cumulative impedance recovery over the last 3 days is 70% or more fromDay 0 of receiving the initial transmission), the intervention is deemedto be successful. If the value of RC is less than or equal to 70, theintervention is deemed unsuccessful and appropriate follow-up action(i.e. second PRN or notification to the investigator) is taken.

If it is determined at block 216 that the patient has recovered from hisworsening HF condition, the YES path continues from block 216 to block220 in which the patient's status of recovery is stored into memory ofserver 130. The process is stopped at block 206 and monitoring forworsening HF condition continues. If the patient is not experiencing arecovery, the NO path from block 216 to block 218 requires a patient'sblood pressure to be checked and a second round of medication to beinitiated. Typically, no additional round of medication is made beyondthe second round of medication. Alternatively, a physician prescribednumber N can be set of rounds medication can be administrated where N isany number from 1 to 10.

At block 218, another determination is made as to whether the patient isexperiencing hypotension or extreme weight gain. The YES path continuesfrom block 222 to block 224, as previously described. The NO path fromblock 222 to block 228 in which the recovery criteria, describedrelative to block 216, is repeated. The YES path from block 228continues to block 230 in which the patient's status of recovery isstored into memory of server 130. The process is stopped 206 andmonitoring continues for worsening HF condition.

The NO path from 228 to block 232 requires that the patient be contactedby medical personnel (e.g. nurse) so that a blood sample can be takenfor evaluation and confirmation that the proper dosage of medication wasprovided. Block 240 also requires a blood sample be taken for evaluationand confirmation that the proper dosage of medication was provided.

At block 234, a determination is made as to whether criteria forbaseline medications are met. The YES path from block 234 to block 238requires the health clinic to evaluate and change the baselinemedication, if necessary. Exemplary baseline medications along withinformation that may be useful for medical personnel are presentedbelow.

If changes to PRN medications are made by a physician, an updatedprescription form must be electronically modified in the system 100 andrecords stored into memory. For example, the updated prescription by thephysician can be sent (i.e. faxed, emailed) to system 100, which willautomatically update the therapy.

Method 200 is stopped at block 236. Method 200 and be repeated over apre-specified period of time designated by the user or pre-specified ina computer program executed by the processor of system 100, and/or oneor more computing devices.

System 100 is configured to automatically acquire, store and analyzedata from computing device(s) and, if the analyzed data indicates thepatient is experiencing worsening HF, system 100 is configured toperform an activity that intervenes into the patient's therapy.Exemplary intervening activities performed by system 100 can includedisplaying, onto a GUI of a computing device, data (i.e. raw data and/oranalyzed data), a list of options to improve the patient's health with abest option highlighted on the GUI, or automatically adjusting thepatient's therapy. Data can be sensed from a variety of devicesincluding implantable devices and/or non-implantable devices (e.g.wearable devices weight scale etc.)

The quality of the signals sensed via the electrodes positioned onnon-implantable devices (e.g. wearable devices, etc.) are typically notthe same as the quality of the signals sensed via the electrodes of theimplantable devices for a variety of reasons. Implantable deviceelectrodes are physically closer to the target tissue (e.g. cardiactissue, etc.) than non-implantable electrodes and such physicalproximity between the electrodes and the target tissue enables theelectrodes to acquire a stronger signal. In addition, electrodes thatare in close proximity to the target tissue may experience less noisecompared to non-implantable electrodes. Nevertheless, non-implantabledevice electrodes provide information that can be useful for trackingand adjusting the patient's therapy and/or lifestyle to improve thepatient's health. Generally, wearable devices can comprise one or moreelectrodes. Exemplary electrodes include skin electrodes, and/or opticalsensors (e.g. optical heart rate sensors located on watches forplacement over the wrist). Exemplary wearable devices with sensorsinclude watches for tracking activities (e.g. Garmin FORERUNNER™,FITBIT™, Apple iWatch™) chest strap with heart sensor and/or heart ratemonitor etc.). The wearable devices are configured to sense data duringphysical activities such as walking, running, biking, swimming or othersport activities. Exemplary health data sensed by the wearable devicemay include heart rate, heart rate variability, blood pressure (e.g.systolic blood pressure, diastolic blood pressure), respiration, bloodoxygen saturation level etc. Wearable devices may also track inactivity(e.g. resting heart rate, sleep activity) and; in addition, distinguishbetween deep or light sleep activities. Health data may continue to besensed and tracked on a twenty-four hour basis by the wearable devicewhile the person is inactive.

In addition to data sensed via wearable electrodes (e.g. skinelectrodes, etc.) other data that affects a patient's health can besensed and monitored. For example, a patient's daily meal may be inputto the device, and, if the patient is not implementing a prescribed mealplan, the patient or health care provider can be automatically alertedthat the nutritional plan is not being followed. An alert can also begenerated to the patient or healthcare provider to indicate supplements(e.g. vitamin or electrolyte(s)) need to be adjusted (i.e. increased ordecreased). Adjusting nutrition and supplements may be important tofunctioning of muscles such as the heart. A patient may be determined tobe outside certain requirements associated with their nutritional planwhen daily designated nutritional values for the patient are not beingmet. For example, consuming too many foods that include high levels ofsodium may cause the patient to exceed daily designated sodium limitsfor the patient. Too much sodium is problematic since sodium affects thepatient's blood pressure. Some patients do not want to receive alertsrelative to sodium. In that case, an undesired alert can be deactivatedto the patient through a GUI of a computing device.

There are many ways in which food or fluid consumption can be input toserver 130. For example, the patient could use a food recognitioncomputer program on a personal digital assistant (e.g. cell phone etc.)that automatically acquires the food consumed by the patient. Exemplarymethods and systems for food recognition and/or caloric data input areshown and described in U.S. Pat. No. 8,439,683 issued May 14, 2013, US20160071431 A1 entitled FOOD DESCRIPTION PROCESSING METHODS ANDAPPARATUSES Al and filed on Sep. 8, 2014, incorporated by reference intheir entirety. In another example, the user may input the meal consumedand estimated amount of calories through a graphical user interface(GUI) on his or her personal digital assistant (e.g. cell phone, Ipad™,computer etc.) Alternatively, the patient could take a picture of themeal. If the entire meal is not consumed, the patient may be able toadjust the amount of calories actually consumed. For example, if theuser consumed only half of the meal, the user could indicate 50% of themeal was eaten and the amount of calories consumed would beautomatically adjusted to indicate 50% of the calories were consumed.Alternatively, another image or picture may be taken and compared topicture taken before the food is eaten. The computer program isconfigured to estimate the amount of calories consumed. Another way inwhich the user may adjust the calories consumed involves user-input ofdata through a graphical user interface associated with a personaldigital assistant (e.g. cellular phone, IPAD™, computer etc.).

Electrolytes (e.g. sodium (Na), magnesium (Mg) potassium (K) etc.), andother nutritional data (e.g. iron, calcium, vitamin A, vitamin C,cholesterol, protein, carbohydrates etc.) can also be acquired andstored into a memory or a database for the purpose of assisting inimproving a patient's health. Electrolytes can be automatically ormanually acquired by server 130. For example, the user can input thedaily amount of electrolytes consumed. The daily amount of electrolytesconsumed may be compared to the patient's required daily amountselectrolytes. Any daily amount of electrolyte that has not met a minimumdaily amount of the daily electrolyte can cause an alert to be generatedto the patient.

Alternatively, electrolytes can be automatically acquired from sensorsassociated with utensils, plates, bowls, containers (e.g. cups,insulated containers for water etc.). For example, utensils used forfood handling (e.g. plates, forks, spoons, knifes and/or other food andliquid containers) can be configured to detect a certain amount ofsodium being consumed, since sodium intake can affect blood pressure.Potassium and magnesium are also important electrolytes to automaticallytrack since potassium and magnesium can affect muscle contraction.

After all of the data has been acquired by server 130 (or processor inthe implantable medical device, portable computer etc.) can compute adynamic risk factor. The dynamic risk factor can be calculatedthroughout the day or over a designated period of time (e.g. number ofdays). Additionally, the risk factor can be stored into memory of theimplantable medical device or other computing device. The risk factor isthen tracked over time to determine whether the patient's health istrending toward a worsening condition. An exemplary risk factorcalculation can comprise U.S. Patent Application No. 62/554,523,entitled DIFFERENTIATION OF HEART FAILURE RISK SCORES FOR HEART FAILUREMONITORING, filed Sep. 5, 2017, incorporated in its entirety herein.

The computed risk score can be used to predict the likelihood of acardiac event occurring within a certain period of time (e.g. the nextthirty days etc.). In one or more embodiments, the computed risk factorcan include a weighted sum calculation that combines data acquired fromimplantable and/or non-implantable electrodes. One weighted sumcalculation can comprise

X=Σ _(x=1) ^(n)(xi*wi)/(Σ_(x=1) ^(n)(wi)

in which w equals a weight, x equals a value for a parameter, “n” is atotal number of values for the parameter and “i” is associated with eachdata. The weighted sum calculation involves (1) multiplying the numbersin a data set by each respective weight associated with the data, (2)the numbers are added from (1) (3) all of the weights are added (4) thenumbers found (2) are divided by the number found in (3).

In one or more embodiments, a dynamic risk score or status calculationcan have a weighted sum automatically adjusted to rely more on dataacquired from one device (e.g. implantable electrodes such assubcutaneous electrodes) compared to another device (e.g. exteriorelectrodes). A variety of methods can be used to adjust the risk scorecalculation. For example, heart rate sensed by a first device (e.g.implantable device configured to sense heart rate) compared to a heartrate of a second device (i.e. wearable device) that are being sensed andare not within a threshold range of each other can cause an automaticincrease of reliance on the IMD electrodes or sensors compared to thewearable device.

In one embodiment, the risk score can be dynamically calculated on areal-time basis using the most currently acquired data. The most currentdata can comprise any designated time period for data obtained fromimplantable and/or non-implantable devices. Exemplary time periods cancomprise the previous 30 days, the previous week, the previous dayand/or data obtained on a real-time basis.

Other embodiments contemplate adjusting the risk score using acorrection factor to adjust for electrodes that may provide lessreliable data. There are a number of ways to determine correction factorfor non-implantable data. For example, one way of determining thecorrection factor may be to apply up to a 10% correction factor tonon-implantable data being used in the risk computation on a real-timebasis. The 10% correction factor is an estimate of the inaccuracy of thenon-implantable electrodes. There are a number of examples that can beused to weight electrodes. In one embodiment, the risk score is weightedby [implantable data×0.85 (and up)+non-implantable data×0.15 (and up)]divided by the number of weighted values. In another embodiment, therisk score is weighted to daily obtained implantable data×0.90 (andup)+non-implantable data×0.10 (and up). In another embodiment, the riskscore is equivalent to daily obtained implantable data×0.95 (andup)+non-implantable data×0.95 (and up). In one embodiment, the riskscore is equivalent to implantable data×0.5+non-implantable data×0.5. Inanother embodiment, the risk score is equivalent to daily obtainedimplantable data×0.85 (and up)+non-implantable data×0.15 (and up).

Another method may involve customizing a patient correction factor to aparticular parameter by using a very reliable method for measuring aparameter and then correlating that measurement to data acquired fromwearable devices, and determining a correction factor for the wearabledata to match the more reliable data. For example, watches areconfigured to estimate heart rate or heart rate variability usingoptical sensors located over the wrist. When the patient is in a healthclinic, the physician may determine the patient's heart rate using ahighly reliable method. For example, the doctor may determine heart rateusing a noninvasive method (e.g. an echocardiogram etc.). While theheart rate or heart rate variability is being calculated using a morereliable method (e.g. implantable electrodes etc.,), the heart rate orheart rate variability can also be determined at or about the same timeusing the optical heart rate monitor located on the watch. Thedifference between the more invasive and less invasive methods forcalculating heart rates can assist in determining the percent that thenon-implantable device is off from the more invasive method. Even if theimplantable electrodes are removed from the patient's body, thecorrection factor can be used with the non-implantable electrodes toestimate a more accurate heart rate. Using the estimated heart rate, anew risk score can be generated by server 130 and used to estimate therisk that the patient will need to be hospitalized within 30 days unlesshis or her therapies are adjusted. After obtaining a new risk score,therapy may be adjusted (e.g. increase pacing for Bradycardia pacing,decrease pacing for Tachycardia patients) and/or another type of actionperformed. For example, pacing can be adjusted. Pacing parameters may bemodified by up to 5% from the prior pacing rate parameter. In yetanother embodiment, pacing can be adjusted by up to 10%. In one or moreother embodiments, medication can be automatically adjusted through anautomatic drug dispenser in response to the new risk score. Prescribedmedication may be limited to diuretics or other non-life-threateningmedication.

In addition to dynamically calculating new risk scores, alerts can beautomatically generated when acquired data indicates that a threshold iscrossed and/or that the threshold is repeatedly crossed over apre-specified period of time. For example, a significant drop orincrease in heart rate or heart rate variability and/or fluid increasingover a pre-specified period of time (e.g. 24 hours) may cause an alertto be generated to the healthcare worker and/or patient that the patientis at risk of being hospitalized within a certain period of time (e.g.1, day, 2 days, . . . up to 30 days). Other alerts may prompt thepatient to perform some action. For example, a sharp increase of heartrate over a certain amount of time without increased physical activity(e.g. running etc.) may indicate that the patient may be subject to acardiovascular event (e.g. heart attack, stroke etc.) In this scenario,the patient and/or healthcare provider would be alerted that the patientneeds to go to a hospital to be evaluated by a physician for animplantable medical device and/or other therapy.

For example, threshold of electrolytes (e.g. too much sodium, potassiumetc.) may be crossed. An alert maybe generated to the patient orhealthcare worker to indicate too much sodium has been consumed or toolittle potassium or water has been consumed. Regardless as to whetherthe patient responds to the alert, the amount of electrolytes maintainedmay affect the therapy that is delivered to the patient.

The data sensed by one or more sensors on and/or associated with thedevice (e.g. IMD, wearable device, external device etc.) can beconfigured to automatically transmit from the device (e.g. implantabledevices, wearable devices, external devices etc.) to server 130. Inaddition, the device (e.g. implantable medical device) can be configuredto transmit data to another device (e.g. another implantable medicaldevice such as Medtronic's LINQ™) that then transmits the data to server130. Server 130 then executes computer instructions to determine whetherthe data crosses a pre-established threshold that may be indicative ofdecreasing cardiovascular health, as previously described. Additionally,server 130 can be configured to track the time period in which thethreshold continued to be crossed.

Many examples exist of employing data from multiple devices to performsome activity. One example of an activity performed in response to theacquired data involves adjusting therapy delivered to the patient inorder to improve the patient's health. Adjusting therapy can beautomatically performed by system 100, an implantable device and/or anexternal device. By way of illustration, an arrhythmia (e.g. atrialfibrillation (AF) etc.) can be detected via a set of electrodes that arestrategically placed on devices (implantable and/or wearable devices(e.g. wearable patch, watch etc.) to obtain physiological signals (e.g.cardiac signals, etc.) in proximity to target tissue. For example, skinelectrodes can be positioned over a target area. Target areas caninclude the wrist to obtain a pulse, on the skin over the heart, and/oron the skin over the carotid artery. Implantable devices have electrodesmuch closer to target tissue and obtain better quality signals than skinelectrodes. After obtaining the signals from one or more sources ofdevices, a computing device (implantable or external) can determinewhether AF is present. In response to detection of AF, drugs that areused for rhythm and/or rate control can be appropriately titrated eitherautomatically by system 100 or manually by a health care professional(e.g. physician) reviewing an alert from system 100 and adjusting thepatient's therapy via a graphical user interface (GUI) associated with acomputing device and electrically coupled to system 100. Dosages ofanti-platelet therapy and/or beta-blockers can be titrated usingprescriptions to control ventricular rate that can be transientlyelevated for the duration of AF through an automatic drug dispense. Sucha strategy will be appropriate for paroxysmal and persistent AF but notfor long-standing persistent AF.

In yet another example, worsening peripheral edema can be detected by animplantable device or external device (e.g. using a patch or smart sock(https://vcea.wsu.edu/eecs2016/smart-sock/) etc.). In response todetecting worsening peripheral edema, the dosage of diuretic can beadjusted appropriately through system 100 acting in an automatic way orthrough sending an alert to a physician to adjust the therapy. Forexample, a smart sock, such as a smart sock that has been developed byWashington State University, may be used to track edema via a set ofsensors that monitor ankle circumference and identify increasedcircumference of the patient's ankle over a short period of time, whichmay be used as an indicator of worsening edema.

Another embodiment relates to blood pressure treatment using dataobtained implantable medical device and/or wearable devices.

One or more embodiments relate to an implantable device based closedlooped blood pressure control for managing hypertension (HTN). Oneventricle can be paced using short AV interval (FIG. 11A) has been shownto reduce arterial blood pressure (FIG. 11B). The mechanisms involvesomewhat premature ventricular contraction that result in decreasedcardiac output (while still meeting the body's demand), and hencedecreased blood pressure. However, the long-term pacing with short AVinterval may be deleterious, and in fact may not be necessary. Forexample, such pacing may lead to atrial dilatation and generate asubstrate for AF.

Referring to FIG. 11, FIG. 11A shows the hypertension (HTN) pacingprotocol and FIG. 11B shows reduction in blood pressure (second bar fromleft) compared to control.

One embodiment involves delivering short-AV pacing in HTN patients onlywhen necessary. To accomplish this, blood pressure will be periodicallymeasured using pulse transit time methodology either using the pacingdevice (that has been modified to incorporate an optical sensor) oraLINQ™ (with optical sensor). In the latter implementation, communicationbetween the two devices will be achieved using TCC or other methodology.Pacing therapy to manage HTN is deployed in a closed loop (FIG. 12)manner only when necessary (i.e. when BP meets set criteria) and basedon the pulse transit time measurements. Specifically, whenever the pulsetransit time indicates that patient's blood pressure has risen above aspecified (programmable) threshold or is showing a rising trend, HTNpacing therapy is deployed (FIG. 2). Such transient increases in bloodpressure may be driven by factors such as patient non-compliance withmedications and other environmental factors. In the LINQ™ implementationof the device, preimplantation with LINQ can be performed tocharacterize BP trends in a patient and to determine whether patient isgood candidate for such closed loop BP control.

EXEMPLARY EMBODIMENTS OF THE DISCLOSURE

The following embodiments are enumerated consecutively from 1 to 20provide for various aspects of the present disclosure. In oneembodiment, in a first (1) paragraph the present disclosure provides amethod for determining whether to intervene with a patient's treatment,the method comprising:

Embodiment 1 is a system for managing patient therapy, the systemcomprising:

(a) an implantable medical device comprising one or more electrodesconfigured to be implanted within a patient's body, to acquire firstsignals corresponding to signals sensed from within the patient's bodyand to generate first data transmissions in response to the acquiredfirst signals;

(b) a wearable device comprising one or more electrodes configured to bepositioned in contact with an external surface of the patient's body, toacquire second signals corresponding to signals sensed from the externalsurface of the patient's body, and to generate second data transmissionsin response to the acquired second signals;

(c) an input/output device to receive the first data transmissions andthe second data transmissions; and

(d) one or more processors configured to:

-   -   (1) receive the first data transmissions and the second data        transmissions,    -   (2) compare the received first data transmissions and the        received second data transmissions to one or more thresholds,    -   (3) determine whether data of the received first data        transmissions and the received second data transmissions is        indicative of a heart failure (HF) worsening episode based on        the comparing, and    -   (4) adjust a patient's therapy in response to the HF worsening        episode being indicated.        Embodiment 2 is a system of embodiment 1 wherein the heart        failure risk score is weighted such that the second data        transmissions is accorded up to 85% weight compared to the first        data transmissions being weighted 100%.        Embodiment 3 is a system of any of embodiments 1 or 2 wherein        the heart failure risk score is weighted such that the second        data transmissions is accorded up to 90% weight compared to the        first data transmissions.        Embodiment 4 is a system of any of embodiments 2 or 3 wherein        the heart failure risk score is weighted such that the second        data transmissions is accorded up to 95% weight compared to the        first data transmissions.        Embodiment 5 is a system of any of embodiments 2 or 4 wherein        the heart failure risk score is weighted such that the second        data transmissions is accorded less weight compared to the first        data transmissions.        Embodiment 6 is a system of any of embodiments 2 or 5 wherein        the heart failure risk score is weighted such that the second        data transmissions is accorded less weight compared to the first        data transmissions wherein fraction xi, is smaller for external        electrodes compared to implanted electrodes, a weighted sum        equation being X=Σ_(x=1) ^(n)(xi*wi)/(Σ_(x=1) ^(n)(wi).        Embodiment 7 is a method for managing patient therapy, the        method comprising:

a. sensing signals from one or more electrodes associated with animplantable medical device disposed within a patient's body;

b. acquiring first signals corresponding to the signals sensed fromwithin the patient's body and generating first data transmissions inresponse to the acquired first signals;

c. sensing signals from electrodes capable of being positioned over anexternal surface of the patient's body, acquiring second signals fromthe external surface of the patient's body and generating second datatransmissions in response to the acquired second signals;

d. receiving the first data transmissions and the second datatransmissions by an input/output device;

-   -   1. receiving the first data transmissions and the second data        transmissions via one or more processors,    -   2. comparing, via the one or more processors, the received first        data transmissions and the received second data transmissions to        one or more thresholds,    -   3. determining, via the one or more processors, whether data of        the received first data transmissions and the received second        data transmissions is indicative of a heart failure (HF)        worsening episode based on the comparing, and    -   4. adjusting, via the one or more processors, a patient's        therapy in response to the HF worsening episode being indicated.        Embodiment 8 is a method of embodiment 7 wherein the heart        failure risk score is weighted such that the second data        transmissions is accorded up to 85% weight compared to the first        data transmissions.        Embodiment 9 is a method of embodiments 7 or 8 wherein the heart        failure risk score is weighted such that the second data        transmissions is accorded up to 90% weight compared to the first        data transmissions.        Embodiment 10 is a method of any of embodiments 7-9 wherein the        heart failure risk score is weighted such that the second data        transmissions is accorded up to 95% weight compared to the first        data transmissions.        Embodiment 11 is a system for managing patient therapy, the        system comprising:

(a) an implantable medical device comprising one or more electrodesconfigured to be implanted within a patient's body, the one or moreelectrodes configured to pace tissue;

(b) a wearable device comprising one or more electrodes configured to bepositioned in contact with an external surface of the patient's body, toacquire second signals corresponding to signals sensed from the externalsurface of the patient's body, and to generate second data transmissionsin response to the acquired second signals;

(c) an input/output device to receive the second data transmissions; and

(d) one or more processors configured to:

-   -   (1) receive the second data transmissions,    -   (2) compare the received second data transmissions to one or        more thresholds,    -   (3) determine whether data of the received second data        transmissions is indicative of a heart failure (HF) worsening        episode based on the comparing, and    -   (4) adjust a patient's pacing therapy in response to the HF        worsening episode being indicated.        Embodiment 12 is a system of embodiment 11 wherein the acquired        signals are solely from one or more electrodes positioned in        contact with the external surface of the patient's body.        Embodiment 13 is a system of any embodiments 11-12 wherein a        battery of an implantable medical device is conserved by solely        using the one or more external electrodes to acquire signals        from the patient's body.        Embodiment 14 is a system of embodiments 11-13 wherein a        processor external to a patient's body causes a signal to be        generated to adjust therapy delivered to the patient.        Embodiment 15 is a system of any embodiments 11-14 wherein a        processor external to a patient's body causes a signal to be        generated to adjust therapy delivered to the patient.        Embodiment 16 is a system of any embodiments 11-15 wherein a        processor, external to a patient's body, determines a patient is        experiencing arrhythmia from the signals from the one or more        electrodes external to the surface of the patient's body.        Embodiment 17 is a system of any embodiments 14-16 wherein the        adjusted therapy comprises changing dosage of a medication.        Embodiment 18 is a system of any embodiments 14-17 wherein the        adjusted therapy comprises the implantable medical device        automatically changing a pacing mode.        Embodiment 19 is a system of any embodiments 14-18 wherein        changing the pacing mode comprises switching between        monoventricular pacing to biventricular pacing. Switching        between monoventricular and biventricular pacing modes can be        based on a variety of different data. Methods or systems for        determining and/or automatically switching from monoventricular        to biventricular pacing modes are exemplarily described and        shown in U.S. Pat. No. 9,789,319, entitled SYSTEMS AND METHODS        FOR LEADLESS CARDIAC REYSNCHRONIZATION THERAPY, FILED Nov. 11,        2013, U.S. Pat. No. 9,403,019, entitled ADAPTIVE CARDIAC        RESYNCHRONIZATION THERAPY, filed Jan. 30, 2012, all of which are        incorporated by reference in their entirety.        Embodiment 20 is a system for managing patient therapy, the        system comprising:

(e) an implantable medical device comprising one or more electrodesconfigured to be implanted within a patient's body and to perform one ofsensing and pacing of tissue;

(f) a wearable device comprising one or more electrodes configured to bepositioned in contact with an external surface of the patient's body, toacquire second signals corresponding to signals sensed from the externalsurface of the patient's body, and to generate second data transmissionsin response to the acquired second signals;

(g) an input/output device to receive the first data transmissions andthe second data transmissions; and

(h) one or more processors configured to:

-   -   (5) receive the second data transmissions,    -   (6) compare the received second data transmissions to one or        more thresholds,    -   (7) determine whether data of the received second data        transmissions is indicative of a heart failure (HF) worsening        episode based on the comparing, and    -   (8) adjust a pacing therapy in response to the HF worsening        episode being indicated.

This disclosure has been provided with reference to illustrativeembodiments and is not meant to be construed in a limiting sense. Asdescribed previously, one skilled in the art will recognize that othervarious illustrative applications may use the techniques as describedherein to take advantage of the beneficial characteristics of theapparatus and methods described herein. Various modifications of theillustrative embodiments, as well as additional embodiments of thedisclosure, will be apparent upon reference to this description.

1. A system for managing patient therapy, the system comprising: (a) animplantable medical device comprising one or more electrodes configuredto be implanted within a patient's body, to acquire first signalscorresponding to signals sensed from within the patient's body and togenerate first data transmissions in response to the acquired firstsignals; (b) a wearable device comprising one or more electrodesconfigured to be positioned in contact with an external surface of thepatient's body, to acquire second signals corresponding to signalssensed from the external surface of the patient's body, and to generatesecond data transmissions in response to the acquired second signals;(c) an input/output device to receive the first data transmissions andthe second data transmissions; and (d) one or more processors configuredto: (1) receive the first data transmissions and the second datatransmissions, (2) compare the received first data transmissions and thereceived second data transmissions to one or more thresholds, (3)determine whether data of the received first data transmissions and thereceived second data transmissions is indicative of a heart failure (HF)worsening episode based on the comparing, and (4) adjust a patient'stherapy in response to the HF worsening episode being indicated.
 2. Asystem for managing patient therapy, the system comprising: (a) animplantable medical device comprising one or more electrodes configuredto be implanted within a patient's body, to acquire first signalscorresponding to signals sensed from within the patient's body and togenerate first data transmissions in response to the acquired firstsignals; (b) a wearable device comprising one or more electrodesconfigured to be positioned in contact with an external surface of thepatient's body, to acquire second signals corresponding to signalssensed from the external surface of the patient's body, and to generatesecond data transmissions in response to the acquired second signals;(c) an input/output device to receive the first data transmissions andthe second data transmissions; and (d) one or more processors configuredto: (i) receive the first data transmissions and the second datatransmissions, (ii) calculate a heart failure risk score such that thefirst data transmissions are weighted differently than the second datatransmissions, (iii) determine whether data of the received first datatransmissions and the received second data transmissions is indicativeof a heart failure (HF) worsening episode based on the comparing, and(iv) displaying one of the worsening HF episode or a risk score on agraphical user interface of a computing device in response to the HFworsening episode being indicated.
 3. The system of claim 2 wherein theheart failure risk score is weighted such that the second datatransmissions is accorded up to 85% weight compared to the first datatransmissions being weighted 100%.
 4. The system of claim 2 wherein theheart failure risk score is weighted such that the second datatransmissions is accorded up to 90% weight compared to the first datatransmissions.
 5. The system of claim 2 wherein the heart failure riskscore is weighted such that the second data transmissions is accorded upto 95% weight compared to the first data transmissions.
 6. The system ofclaim 2 wherein the heart failure risk score is weighted such that thesecond data transmissions is accorded less weight compared to the firstdata transmissions.
 7. The system of claim 2 wherein the heart failurerisk score is weighted such that the second data transmissions isaccorded less weight compared to the first data transmissions whereinfraction xi, is smaller for external electrodes compared to implantedelectrodes, a weighted sum equation being X=Σ_(x=1) ^(n)(xi*wi)/(Σ_(x=1)^(n)(wi).
 8. A method for managing patient therapy, the methodcomprising: a. sensing signals from one or more electrodes associatedwith an implantable medical device disposed within a patient's body; b.acquiring first signals corresponding to the signals sensed from withinthe patient's body and generating first data transmissions in responseto the acquired first signals; c. sensing signals from electrodescapable of being positioned over an external surface of the patient'sbody, acquiring second signals from the external surface of thepatient's body and generating second data transmissions in response tothe acquired second sianals; d. receiving the first data transmissionsand the second data transmissions by an input/output device; 1.receiving the first data transmissions and the second data transmissionsvia one or more processors,
 2. comparing, via the one or moreprocessors, the received first data transmissions and the receivedsecond data transmissions to one or more thresholds,
 3. determining, viathe one or more processors, whether data of the received first datatransmissions and the received second data transmissions is indicativeof a heart failure (HF) worsening episode based on the comparing, and 4.adjusting, via the one or more processors, a patient's therapy inresponse to the HF worsening episode being indicated.
 9. The method ofclaim 8 wherein the heart failure risk score is weighted such that thesecond data transmissions is accorded up to 85% weight compared to thefirst data transmissions.
 10. The method of claim 8 wherein the heartfailure risk score is weighted such that the second data transmissionsis accorded up to 90% weight compared to the first data transmissions.11. The system of claim 8 wherein the heart failure risk score isweighted such that the second data transmissions is accorded up to 95%weight compared to the first data transmissions.
 12. A system formanaging patient therapy, the system comprising: (a) an implantablemedical device comprising one or more electrodes configured to beimplanted within a patient's body, the one or more electrodes configuredto pace tissue; (b) a wearable device comprising one or more electrodesconfigured to be positioned in contact with an external surface of thepatient's body, to acquire second signals corresponding to signalssensed from the external surface of the patient's body, and to generatesecond data transmissions in response to the acquired second signals;(c) an input/output device to receive the second data transmissions; and(d) one or more processors configured to: (1) receive the second datatransmissions, (2) compare the received second data transmissions to oneor more thresholds, (3) determine whether data of the received seconddata transmissions is indicative of a heart failure (HF) worseningepisode based on the comparing, and (4) adjust a patient's pacingtherapy in response to the HF worsening episode being indicated.
 13. Thesystem of claim 12 wherein the acquired signals are solely from one ormore electrodes positioned in contact with the external surface of thepatient's body.
 14. The system of claim 12 wherein a battery of animplantable medical device is conserved by solely using the one or moreexternal electrodes to acquire signals from the patient's body.
 15. Thesystem of claim 12 wherein a processor external to a patient's bodycauses a signal to be generated to adjust therapy delivered to thepatient.
 16. The system of claim 12 wherein a processor, external to apatient's body, determines a patient is experiencing arrhythmia from thesignals from the one or more electrodes external to the surface of thepatient's body.
 17. The system of claim 12 wherein the adjusted therapycomprises changing dosage of a medication.
 18. The system of claim 12wherein the adjusted therapy comprises the implantable medical deviceautomatically changing a pacing mode.
 19. The system of claim 19 whereinchanging the pacing mode comprises switching between monoventricularpacing to biventricular pacing.
 20. A system for managing patienttherapy, the system comprising: (a) an implantable medical devicecomprising one or more electrodes configured to be implanted within apatient's body and to perform one of sensing and pacing of tissue; (b) awearable device comprising one or more electrodes configured to bepositioned in contact with an external surface of the patient's body, toacquire second signals corresponding to signals sensed from the externalsurface of the patient's body, and to generate second data transmissionsin response to the acquired second signals; (c) an input/output deviceto receive the first data transmissions and the second datatransmissions; and (d) one or more processors configured to: (1) receivethe second data transmissions, (2) compare the received second datatransmissions to one or more thresholds,